Remote Work and Employee Attitudes: The Role of Reward Allocation in European SMEs

Abstract

Digital transformation and remote work continue to reshape the nature of work, yet the implications for salaries and promotions in SMEs continue to be poorly understood. This study investigates how reward allocation mediates the relationship between remote work and employee attitudes, addressing the organizational challenges of aligning human resource practices with digitalization work models in the European context based on the social exchange theory and data collected from n = 615 participants, primarily from SMEs, which were analyzed using PLS-SEM software and WarpPLS 7.0. The findings reveal that remote work not only strengthens workers’ engagement and satisfaction but also channels their efforts toward achieving outcomes and reward allocation, reducing the reliance on physical presence and visibility. Digital transformation enables SMEs to adopt more flexible and fairer HR practices, with remote work also contributing to organizational resilience, employee agility, and competitiveness. This research advances the theoretical understanding of HRM in the context of digital transformation, offering SMEs a guide to building a suitable reward system that promotes both individual well-being and sustainability.

1. Introduction

Digital transformation is reshaping the organizational landscape and how European SMEs organize work, distribute rewards, and manage people (Orlandi et al., 2024). In this context, the adoption and integration of information and communication technologies (ICTs) play a crucial role in transforming traditional work settings, introducing a new format for coordinating and collaborating with people (Donnelly & Johns, 2020). To address these changes, organizations resort to digital transformation, which enables them to be more flexible, introduce new benefits, and develop new business models (Schneckenberg et al., 2021). The adoption of remote work in SMEs is not only a cost-saving measure but also a strategic adaptation that enables digital technologies, helping them to be competitive and flexible in volatile environments (Torre & Sarti, 2019) and allowing workers to carry out their activities and tasks remotely beyond the confines of an office (Orlandi et al., 2024). Remote work offers significant benefits, including autonomy, reduced working hours, improved work–life balance, cost savings, and enhanced employee retention (Charalampous et al., 2018; Alifuddin & Ibrahim, 2021; Delanoeije & Verbruggen, 2020). However, it adversely affects employee well-being, including overwork, negative emotions, gender labor division, gender discrimination, and stress, which could lead them to underperform and create an organizational crisis (Genquiang et al., 2024). As a result, it leads to anxiety and worries about their survival and future development and job insecurity, prompting employees to ignore ethical boundaries and unquestioningly adopt unethical behaviors (Thau et al., 2015; Shoss, 2017). In light of this statement, the study highlights two critical gaps. The first relates to the limited research on how remote work affects reward allocation, specifically salaries and promotions. The second refers to the limited understanding of how digital transformation enables remote work in SMEs, influencing employee attitudes in the European context. By understanding and addressing these gaps, the following research question was posed: How does digital transformation-enabled remote work influence reward allocation mechanisms and their impact on employee attitudes in SMEs? By answering this question, our study makes four contributions. First, we extend remote work theory to the context of SME digital transformation through the identification of mechanisms like promotions and salary functions as performance-based drivers of fairness and employee development (Aggarwal, 2024). Second, we provide region-specific (on the European continent) insights into the organizational implications of remote work under technological maturity levels, cultural norms, and distinct labor regulations. Third, the validation of reward allocation in the relationship between remote work and employee attitudes provides mechanisms that explain digital transformation and work models that weigh organizational behavior in SMEs. Fourth, the highlighted link between reward systems and remote work conditions integrates social exchange theory with dynamic capabilities theory. With this in mind, the paper is structured as follows: In Section 1, we present the introduction, and in Section 2, we address the theoretical framework and the development of the hypothesis. In Section 3, we describe the research methodology. Section 4 is reserved for the research findings. In Section 5, we present the discussion and conclusions. Section 6 presents the research implications, and Section 7 presents theoretical and practical implications. Section 8 presents limitations and future research.

2. Theoretical Framework

2.1. Remote Work and Digital Transformation

With the growing advances of digital transformation, an increasing number of SMEs are developing innovation strategies centered on digital organizational structures to improve their processes and increase competitiveness, as digital transformation can be seen as a process of building or participating in digital platform ecosystems to drive value and market expansion (Astorquiza-Bustos & Quintero-Peña, 2023). The process of transformation can blur traditional physical boundaries to acquire heterogeneous resources while simultaneously introducing competitive economic activities and new challenges to value creation propositions (Cenamor et al., 2019; Gandrita, 2024). In this context, organizations need to reconfigure their coordination, communication, and delivery formats to extend their boundaries beyond physical offices.

Remote work has become the solution for this reconfiguration and has increased significantly over the past few years due to the widespread adoption of modern technologies (Kerman et al., 2022), which have substantially improved and simplified remote collaboration and communication (Kossek et al., 2012). This shift has become the “new normal”, particularly following the COVID-19 pandemic, when organizations were forced to change their work paradigms and operational models (Iogansen et al., 2024). Remote work has emerged as a solution for maintaining operations that are becoming increasingly flexible, allowing organizations to adapt to rapidly changing environments (Torre & Sarti, 2019).

Therefore, this new work setting should be considered an organizational model that allows employees to perform their tasks from locations outside the traditional setting, enabling them to carry out their activities and functions remotely (Orlandi et al., 2024). While this technology offers solutions for organizations, it has both positive and negative aspects that managers should address to ensure workers’ continuous well-being (Perry et al., 2018).

In the broader context of remote settings, internal stakeholders have identified several negative outcomes regarding areas such as salary adjustments (Stoddard, 2005; Lu & Niu, 2022), workforce promotion (Powell, 2006; Yadav & Dhar, 2021), employee engagement (Pattnaik & Jena, 2020; Saks, 2021), commitment (Huang et al., 2010; Scheuer & Thaler, 2024; Pham et al., 2024), job satisfaction (Gellis, 2001; Pugh, 2016; Daouda et al., 2021), and worker performance (Hoey et al., 2023; Okawara et al., 2023). On the other hand, managers highlight that implementing this technology has a positive impact on productivity, employee satisfaction, and organizational performance (Chatterjee et al., 2022). Nevertheless, both the positive and negative aspects of remote work hold weight in shaping reward allocation and employee attitude. Managers must carefully balance these factors to ensure that the use of this technology promotes productivity and employee well-being while addressing the challenges associated with this work model (Ng et al., 2022).

2.2. Influences of Remote Work and Reward Allocation

While remote work has become a globally adopted practice, much of the existing literature review focuses on North America or presents generalized global perspectives. To address this gap, we restrict the study to the European context, where unique labor laws, cultural norms, and organizational practices shape reward-allocation mechanisms (Capone et al., 2024).

In this study, we identified two factors related to reward allocation, (1) salaries and (2) promotions, which can also be explained through social exchange theory. This theory suggests that incentives and collaboration improve and foster employee attitudes (Joshi, 2025), reinforcing their development and performance. Remuneration has become one of the most debatable and controversial subjects for employees (Day, 2011) due to its pervasive and influential form of motivation directly affecting workers’ behavior and attitudes towards the company (Corgnet et al., 2019). According to Li and Roloff (2008), this factor is one of the most significant tools in human resource management, playing a crucial role in retaining top talent and employees.

During our research, we observed that salaries have a positive influence on employee attitude, and in some cases, an impact on organizational performance (Kim & Jang, 2020). Furthermore, to maintain competitiveness, managers need to improve employee salaries to attract and retain intellectual property and a skilled workforce (Magnan & Martin, 2019).

To provide a complementary lens in this study, we incorporate equity theory (Soltani Mandolakani & Singleton, 2024), which posits that individuals evaluate fairness by comparing their inputs (skills and effort) and outcomes (promotion and salary) to those of others (Lim, 2020). However, in most situations, managers do not consider remote work a valid reason for salary increase. Many organizations may view remote work as a benefit, offering flexibility and reducing commuting costs, thereby offsetting the need for higher pay (Crowley & Doran, 2020). The second factor concerns promotions, which are typically driven by the need for “advancement,” accomplishment, and growth (Wang et al., 2024). This action encourages change, development, and exploration of novel and creative behaviors (Neubert et al., 2008) among future managers and employees who aspire to advance in their careers.

According to Brenninkmeijer and Hekkert-Koning (2015), promotion-focused employees adopt a more flexible approach to work, demonstrating adaptability, openness to experience, and a willingness to go beyond prescribed roles to seek ideal outcomes.

However, to the best of our knowledge, the relationship between promotions and remote work remains unexplored. There is a persistent concern that the lack of visibility and in-person interaction with managers may negatively impact promotion opportunities (Stoddard, 2005).

Moreover, the growing interest in remote work practices and flexible work arrangements has led some organizations to reevaluate their promotion criteria, focusing more on results, innovation, and adaptability, qualities often demonstrated by remote workers (Ng et al., 2022). This evolving perspective suggests that remote work has a positive impact on workforce promotion, particularly in organizations that embrace digital transformation and remote work as a core part of their operational model. In this sense, as we build on our evolving understanding of reward allocation in remote work contexts, this research seeks to empirically test two critical components (salary adjustments and workforce promotions) of reward allocation within the European context, where unique cultural and organizational dynamics remain largely unexplored. Therefore, the following hypothesis is proposed:

H1. 

Remote work positively influences salary adjustments in SMEs.

H2. 

Remote work positively influences workforce promotions in SMEs.

2.3. Remote Work and Employees’ Attitudes in SMEs

Factors underlying employee attitudes in SMEs have attracted the attention of scholars such as Bhusan (2024) and Shdaifat et al. (2023), have identified four essential factors in their studies, namely, (1) commitment, (2) engagement, (3) job satisfaction, and (4) performance, as the most prevalent in the current organizational context.

First, employee commitment is defined as a state in which an employee identifies with a specific organization’s values and goals (Triguero-Sánchez et al., 2022), reducing the likelihood of leaving the organization (Allen & Meyer, 2000). Such a commitment is related to the intensity of employees’ sense of belonging and involvement, which has a positive impact on individual work performance (Kaiser et al., 2025).

The relationship between remote work and commitment has been investigated by authors such as Taboroši et al. (2020), Alshibly & Alzubi (2022), and Kortsch et al. (2022). According to Adamovic et al. (2022), remote work can serve as a personal job resource, enabling the balancing of new work demands, space reconfiguration, innovation, collaboration, and flexible working conditions (Angelici & Profeta, 2023).

When employees practice RW, supervision is often absent at home (Balamurugan & Priyanka, 2023). Consequently, workers need to effectively manage their time, structure their work, and set clear goals to continue developing proficiency in some of their daily tasks (Orlandi et al., 2024). In addition, empirical evidence has been found on the spillover effect (Ahluwalia et al., 2001), referring to positive behaviors and attitudes in one area, such as work-related autonomy and job satisfaction, influencing other areas, including employee commitment and performance (Beehner & Blackwell, 2016).

Employee commitment improves when remote work fosters a positive relationship that does not undermine engagement, thus leading to high levels of effectiveness in remote work environments. The second factor, engagement, is described as employee persistence, cognition, and a positive state of fulfillment (Schaufeli et al., 2002a). However, Schaufeli et al. (2002b) reports a satisfying and favorable mental state toward work, characterized by three essential elements: dedication, absorption, and vigor. The curiosity surrounding this phenomenon has led researchers to study it and deepen their understanding of commitment. As a result, Podder & Saha (2024) concluded that when employees are engaged, they tend to be highly self-efficacious, energetic, and more committed.

Additionally, when employees are engaged, they contribute positively to their organization by retaining and attracting support from coworkers and new customers (Okafor & Chimereze, 2020). Therefore, increasing employee engagement is crucial (Okojie et al., 2024; Gandrita et al., 2025b), especially in a remote work setting. Furthermore, organizations that adopt this technology demonstrate greater agility and resilience, turning remote work into a strategic capability. This complement connects to dynamic capabilities theory (He & He, 2025), which can be seen as a core mechanism for reconstructing and integrating internal and external resources in response to environmental changes (Soluk & Kammerlander, 2021).

In addition, from a human perspective, employees who receive resources and benefits from the organization and their managers tend to be more motivated and engaged in the workplace (Zhong et al., 2016). However, Molino et al. (2020) noted that one of their concerns with a shift to remote work is how worker engagement may be affected by stress.

Others, such as Perry et al. (2018) and Kirchner et al. (2021), discuss the lack of supervision and control. Finally, Yang et al. (2022) address the significant decline in internal organizational communication, which could lead to lower emotional investment. Given the possibility of a remote work setting and the constraints it entails, the key is to increase an employee’s engagement in the organization, creating conditions (May et al., 2004) that foster a better work environment and job satisfaction centered on their needs.

The third factor is job satisfaction, a key element and one of the most crucial factors in the organization’s progress (Günday et al., 2022). Defined by Gellis (2001) and Pugh (2016), job satisfaction is an employee’s positive feelings about their work position. Al Sabei et al. (2020) discuss job satisfaction as a positive attitude and a set of emotions that people have towards their jobs. However, the author emphasized the importance of employees who do not feel diminished, as they may otherwise quit.

According to Çam & Yıldırım (2010), several factors influence job satisfaction, which can be generally categorized at both individual and organizational levels. Personal factors that affect job satisfaction include age, gender, marital status, personality traits, education, and the socio-cultural environment (Tenaw et al., 2021). On the other hand, institutional factors encompass working conditions within the organizational environment, management systems, salaries, relationships with managers, development opportunities, workplace communication, and opportunities for improvement (Al Sabei et al., 2020).

These factors are related to motivation theory, which posits that organizational factors are linked to job satisfaction (Zhang et al., 2024). It is also considered a valuable factor (job association) in the development and maintenance of an adequate level of satisfaction (Udayasuriyan & Balaji, 2023). In the context of remote work, employees must address their concerns to perform their jobs effectively and achieve job satisfaction. Some of these hesitations include sharing personal information with their managers, interacting with every member of the organization, attending work-related meetings, and overcoming work isolation (Downes et al., 2024).

Employee performance in the workplace is determined by the overall organization’s efficiency and productivity (Sobaih et al., 2019). As previously stated, remote work allows employees to perform their tasks from locations outside the office (Orlandi et al., 2024).

Based on the literature, the hypotheses are as follows:

H3. 

Salary adjustments have a positive effect on employee attitudes.

H4. 

Workforce promotions have a positive effect on employee attitudes in SMEs.

H5. 

Remote work has a positive effect on employees’ attitudes.

H6. 

Reward allocation mediates the relationship between remote work and employees’ attitudes.

Based on the above hypothesis, it was determined that remote work affects salary adjustments and promotions, which in turn have a direct or indirect impact on commitment, engagement, job satisfaction, and performance. In that sense, the following conceptual model (Figure 1) was determined as follows:

3. Methodology

3.1. Questionnaire and Measurements

Based on our research model, a structured questionnaire was developed to collect data from the European participants. The questionnaire has two main parts. The first comprised the characteristics of the respondents, including gender, age, education, country, industry, department, work experience, industry type, and company size. Section 2 includes the measurement of reward allocation (linked to SA and WFP), employee attitudes (linked to COM, ENG, JS, and WP), and remote work.

The reason for our choice for the items describing the reviewed constructs was based on previous studies: remote work (15 items; Yun et al., 2020), commitment (10 items; Huang et al., 2010; Scheuer & Thaler, 2024; Pham et al., 2024), engagement (10 items; Saks, 2021; Pattnaik & Jena, 2020), job satisfaction (5 items; Gellis, 2001; Pugh, 2016; Daouda et al., 2021), salary adjustment (7items; (Stoddard, 2005; Lu & Niu, 2022), workforce promotion (7 items; Powell, 2006; Yadav & Dhar, 2021), and performance (7 items; Hoey et al., 2023; Okawara et al., 2023). All items in this study were measured using a 7-point Likert scale (1 = “strongly disagree” to 7= strongly agree”).

Dawes (2008), Altuna & Arslan (2016), and Dolnicar (2021) recommended this scale, which is more accurate than a 5-point Likert scale. In addition, to create a more precise questionnaire with relevant information, the survey was sent to five professors specialized in management. The experts reviewed the content by providing feedback that led to improvements in the questionnaire before it was sent to the participants.

3.2. Scale Measurement

3.2.1. Reward Allocation

Reward allocation was measured using a scale adapted from previous studies on organizational rewards (Stoddard, 2005; Lu & Niu, 2022; Hoey et al., 2023; Okawara et al., 2023). This measure includes two variables (salary adjustments and workforce promotions) that are distributed based on organizational performance. Salary adjustments were measured with items such as “I believe that salary adjustments are fairly distributed based on performance in the organization,” and workforce promotion was measured with items like “I believe that promotions in my organization are merit-based and equitable.”

3.2.2. Employee Attitudes in SMEs

Employee attitudes were measured using a combination of established scales. This variable includes dimensions such as commitment, engagement, job satisfaction, and performance. Commitment was measured in items like “I am loyal to my organization.” Engagement was measured using items such as “I feel enthusiastic about my work” and “I am fully absorbed in my task when working remotely.” Job satisfaction included items such as “I am satisfied with my current job responsibilities,” and performance included items like “I am willing to put in extra effort for the company’s success.”

3.2.3. Control Variables

Our study also considered control variables that could affect our results, namely, age, gender, education level, and work experience. For age measurement, we adopted the following categories. Age is considered a critical variable, as it can influence employee attitudes and career progression. Younger employees may have different perspectives, expectations, and adaptability to remote work than older employees do. Considering the number of positions in an organization, we used the gender variable to account for potential disparities in salary adjustments and promotions.

To measure work experience, we considered the following ranges: less than 5 years, 5–10 years, and more than 10 years. This variable may consider the levels of competence and job autonomy, which affect workers’ ability to work remotely and influence their likelihood of receiving promotions and salary adjustments. An academic degree determined whether the employee held a Ph.D., master’s, bachelor’s degree, secondary education, or basic education. These variables are correlated with job role, skill level, and career trajectory. Employees with higher expectations for promotions and salaries have positioned themselves to benefit from remote work arrangements. SMEs will be measured across three levels: early adopters, scaling adopters, and digitally mature companies. This variable (SMEs) reflects the organization’s capacity to integrate digital technologies, adapt to changing business models, and leverage remote work to enhance competitiveness and growth.

3.3. Sampling and Data Gathering

As previously mentioned, our study focuses on evidence from the European population, as this context provides a unique lens for examining the impacts of remote work on reward allocation and employee attitudes. The choice of the target population was driven by the exponential rise in organizations adopting remote work (Tahlyan et al., 2024) as a valuable option, despite its widespread use during the COVID-19 pandemic. The online questionnaire used was Google Forms and was distributed from January to May of 2025. Initially, 800 invitations were screened, but only 615 responses were validated.

Due to the population’s location, a purposive sampling technique will be employed in this study to select participants who meet specific criteria, such as remote workers in various industries across the European continent, thereby ensuring the sample is relevant to the research objectives. This study employed an online survey to collect answers from the participants, which was anonymous and voluntary. Invitations were sent via LinkedIn, WhatsApp, email (Gandrita et al., 2025a), and other social media platforms due to the nature of this study. Participation represented a multitude of sectors, helping to provide a diversified perspective on remote work dynamics within SMEs.

However, only 615 responses were validated, mainly from Poland (25.37%), Portugal (28.29%), and the United Kingdom (19.51%).

Table 1. Sociodemographic profile.

Characteristic	Classification	Frequency (n = 615)	Percentage (100%)
Gender	Female	225	36.59
	Male	390	63.41
Age	18–25	198	32.2
	26–35	261	42.44
	36–45	90	14.63
	46–55	42	6.83
	56–65	15	2.44
	Over 65	9	1.46
Country	Portugal	156	25.37
	Poland	174	28.29
	United Kingdom	120	19.51
	Spain	60	9.76
	Greece	57	9.27
	Belgium	6	0.98
	Germany	12	1.95
	France	12	1.95
	Ireland	9	1.46
	Finland	3	0.49
	Netherlands	3	0.49
	Lithuania	3	0.48
Education	Basic education	6	0.98
	Secondary	150	24.39
	Bachelor’s degree	219	35.61
	Master’s degree	198	32.2
	Ph.D.	27	4.39
	Other	15	2.43
Industry	Technological	204	33.17
	Management	36	5.85
	Pharmaceutical	21	3.41
	Logistics	27	4.39
	Retail	39	6.34
	Education	69	11.22
	Tourism	24	3.9
	Food and Beverage	9	1.48
	Electronic	15	2.44
	* Other	171	27.8
Department	Financial	45	7.32
	Human Resources	45	7.32
	Sales	69	11.22
	Logistics	42	6.83
	Marketing	36	5.85
	* Other	378	61.46
Work Experience	Less than 5 Years	285	46.34
	5–10 years	171	27.7
	More than 10 Years	159	25.85
Institution Type	Private	429	69.76
	Public	153	24.88
	Non-profit	33	5.36
Company Size	SMEs	384	62.44
	Large	231	37.56
Total		615	100%

Note: (*) Refers to industries/departments/institution types not listed among the predefined categories.

presents the demographic profile showing that 63.41% of all respondents were male and 36.59% were female.

Regarding ages, 32.2% were between the ages of 18 and 25 years old, 42.44% between ages 26 and 35 years old, 14.63% were 36–45 years old, 6.83% were 46–55 years old, 2.44% were 56–65 years old, and finally 1.46% were > 65 years old.

The countries with higher rates are Poland, Portugal, and the United Kingdom, while those with a lower rate include Spain, Greece, Belgium, Germany, France, Ireland, Finland, Lithuania, and the Netherlands. Participants were primarily employed in the technology (33.17%) and education (11.22%) industries, which have higher rates of remote work. Most participants were in the sales department (11.22%). Regarding the level of education, the participants with higher rates were bachelor’s degree with 35.61%, master’s degree with 32.2%, and secondary education with 24.39%.

Regarding work experience, 46.34% had worked in the organization for less than five years, 27.8% for 5–10 years, and 25.85% for more than ten years. Regarding institution type, 69.76% were from the private sector, 24.88% from the public, and 5.37% from a non-profit organization. In addition, 62.44% of the participants worked in SMEs and 37.56% worked in large corporations.

3.4. Data Analysis

Considering the nature of the causal relationships among the latent variables, we employed partial least squares structural equation modeling (PLS-SEM) with WarpPLS 7.0 software package (Kock, 2020). This software is particularly suitable for this study because of its ability to model complex relationships between multiple latent variables, including remote work, reward allocation (e.g., salary adjustments, workforce promotion), employee attitude (e.g., engagement, commitment, job satisfaction, and performance), and control variables (age, gender, education level, work experience, and SMEs). Given the exploratory nature of the study and its focus on theory development rather than strict theory confirmation, this software provides an appropriate methodological framework.

Moreover, the sample size of n = 615 aligns with the robustness of PLS-SEM (Akter et al., 2024; Vaithilingam et al., 2024) in handling limited datasets, as opposed to covariance-based SEM, which often requires a large sample. In addition, it accommodates non-normal data distributions and enables the simultaneous evaluation of measurement and structural models, ensuring both construct validity and model predictive power. Previous research on remote work has demonstrated the utility of PLS-SEM in analyzing complex frameworks involving attitudinal outcomes, further supporting its application in this context (Kurtaliqi et al., 2024; Du & Fujiwara, 2024).

4. Results

4.1. Measurement Model Testing

Several steps were implemented to test the measurement (outer) model. First, to establish the reliability indicator, the outer loadings of each latent variable should have a cut-off point of at least 0.60. In this sense, we excluded every load that scored less than 0.60 (i.e., RW7, COM5, SA2, SA8, WFP3, WFP5, WFP8, and JS3).

As shown in

Table 2. Measurement model assessment.

	Variable	Indicator	Factor Loading	AVE	α	CR
	Remote work (RW)			0.411	0.753	0.826
		RW1	0.614
		RW2	0.745
		RW3	0.619
		RW4	0.515
		RW5	0.758
		RW6	0.709
		RW7	0.469
Reward Allocation	Salary Adjustments (SA)			0.400	0.734	0.814
		SA1	0.717
		SA2	0.173
		SA3	0.744
		SA4	0.675
		SA5	0.774
		SA6	0.700
		SA7	0.746
		SA8	0.045
	Workforce Promotion (WFP)			0.386	0.730	0.811
		WFP1	0.775
		WFP2	0.696
		WFP3	0.409
		WFP4	0.745
		WFP5	0.283
		WFP6	0.792
		WFP7	0.745
		WFP8	0.143
Employee Attitudes	Commitment (COM)			0.502	0.819	0.869
		COM1	0.735
		COM2	0.626
		COM3	0.757
		COM4	0.782
		COM5	0.285
		COM6	0.806
		COM7	0.815
	Engagement (ENG)			0.482	0.820	0.866
		ENG 1	0.683
		ENG 2	0.690
		ENG 3	0.717
		ENG 4	0.663
		ENG 5	0.704
		ENG 6	0.758
		ENG7	0.636
	Job Satisfaction (JS)			0.532	0.850	0.887
		JS1	0.787
		JS2	0.677
		JS3	0.536
		JS4	0.772
		JS5	0.777
		JS6	0.742
		JS7	0.782
	Worker Performance (WP)			0.607	0.835	0.885
		WP1	0.814
		WP2	0.864
		WP3	0.704
		WP4	0.675
		WP5	0.822

Note: FL = Factor Loading; CR = Composite Reliability; AVE = Average Variance Extracted.

, the composite reliability (CR) values exceeded 0.7, indicating the good internal consistency of the constructs. The average variance extracted (AVE) values are above 0.5, indicating that the constructs capture more than half of the variance in their indicators, a strong sign of convergent validity (Gandrita et al., 2022). These results demonstrate that the model meets the requirements of construct reliability and validity. The AVE values exceeded 0.5, namely commitment (0.502), job satisfaction (0.606), JS (0.532), and performance (0.607). However, some constructs, namely remote work, salary adjustments, workforce promotion, and engagement, presented AVE slightly below the recommended threshold of 0.5. These values do not compromise convergent validity due to Cronbach’s alpha and CR, for which all constructs exceeded the value of 0.70. (Hair et al., 2020). Although AVE is a conservative criterion, a value below 0.50 may be acceptable when reliability is high (Fornell & Larcker, 1981). The lower values of AVE are a consequence of a large number of indicators and moderate loading with some items. A re-analysis was performed by removing the lowest-loading items, which improved the AVE value without compromising reliability. Therefore, all the constructs were retained to preserve theoretical comprehensiveness and comparability with prior studies. Additionally, discriminant values were confirmed through the HTMT ratio, which remained below the recommended threshold, supporting the robustness of the measurement model.

Regarding discrimination validity, the Heterotrait–Monotrait (HTMT) approach was considered superior to Fornell and Larcker’s (1981) approach.

Table 3. Discriminant validity—HTMT ratio.

Construct	RW	SA	WFP	COM	ENG	JS	WP
RW	1
SA		1
WFP		0.597	1
COM		0.452	0.728	1
ENG		0.586	0.657	0.549	1
JS		0.564	0.757	0.599	0.720	1
WP		0.491	0.856	0.885	0.765	0.833	1

shows that the HTMT values for the latent variables were below 0.90, except for Henseler et al.’s (2015), but with significance lower than 1.0.

Furthermore, discriminant validity was established. In addition, in relation to the goodness-of-fit indices shown in

Table 4. Model fit and quality indices.

Average path coefficient (APC)	0.682	p < 0.001
Average R-Square (ARS)	0.473	p < 0.001
Average adjusted R-Square (AARS)	0.471	p < 0.001
Average full collinearity VIF (AFVIF)	2.114	<5.0, acceptable
Tenenhaus GOf (GoF)	0.523	>0.36, large
Sympson’s paradox ratio (SPR)	1.000	Ideally
R-Squared contribution ratio (RSCR)	1.000	Ideally
Statistical suppression ration (SSR)	1.000	Ideally
Nonlinear bivariate causality direction ratio (NLBCDR)	1.000	Acceptable if >0.7

, the fit indices of APC, ARS, AFVIF, AARS, GoF, SPR, RSCR, SSR, and NLBCDR were acceptable and adequate (Kock, 2020).

4.2. Structural and Model Assessing

To understand the causal relationships between the latent variables investigated, the structural model was determined based on the following measures: path coefficients and their significance (p-value), R², Q², and size (f²). Figure 2 shows a representation of the structural model based on these results.

According to them, remote work had a positive and significant impact on salary adjustment (β = 0.073, p < 0.001), workforce promotion (β = 0.093, p < 0.020), commitment (β = 0.072, p < 0.024), engagement (β = 0.310, p < 0.001), job satisfaction (β = 0.088, p < 0.028), and performance (β = 0.207, p < 0.001).

As a result, all hypotheses were supported based on the values provided (R²) of the endogenous constructs. Remote work has a value of 0.591%, indicating a moderate level of explanatory power.

In the analysis, effect sizes (f²) were evaluated using guidelines, where values between 0.02 and 0.15 indicate a low effect size, values between 0.15 and 0.35 indicate a medium effect size, and values greater than 0.35 represent a large effect size.

Based on this analysis, we calculated the effect size (f²) for the path coefficients. According to Hair et al. (2020), the f² values are as follows: The paths from RW to SA, WFP, and COM showed a low effect size, with f² values of 0.009 and 0.005, respectively. These results indicate that remote work substantially affects both salary adjustment, workforce promotion, and commitment. Remote work and engagement had a small to medium effect size (f² = 0.115). The RW and JS path of f² = 0.025 represents a small effect size, influencing modest engagement. Similarly, the relationship between remote work and WP (f² = 0.047) also exhibited a small effect size. The results in

Table 5. Structural model and hypothesis testing.

Hypothesis		Path Coefficients (β)	p-Value	Effect Size (f2)	Results
H1	RW → SA	0.073	<0.001	0.005	Supported
H2	RW → WFP	0.093	<0.020	0.009	Supported
H3	RW → COM	0.072	<0.024	0.005	Supported
H4	RW → ENG	0.310	<0.001	0.115	Supported
H5	RW → JS	0.088	<0.028	0.025	Supported
H6	RW → WP	0.207	<0.001	0.047	Supported

show that low to medium effect sizes were found among all links between the latent variables. Subsequently, we assessed the Q² values. The values of this measure should exceed zero to be considered medium, ≥0.25 meaningful, and ≥0.50 large. The values obtained are listed in Table 5.

Moreover, in

Table 6. Multi-group analysis (MGA).

Hypothesis	Path Coefficients (β)	(β)	f2		α
H1	RW → SA	0.073	0.005	Low effect size	0.734
H2	RW → WFP	0.093	0.009	Low effect size	0.730
H3	RW → COM	0.072	0.005	Low effect size	0.819
H4	RW → ENG	0.310	0.115	Small to Medium effect	0.820
H5	RW → JS	0.088	0.025	Small effect size	0.850
H6	RW → WP	0.207	0.047	Small effect size	0.835

Note: SA = Salary Adjustments, WFP = Workforce Promotion, COM = Commitment, ENG = Engagement, JS = Job Satisfaction, and WP = Worker Promotion.

, we conducted a multi-group analysis (MGA) to compare the impact of remote work across the six hypotheses related to reward allocation and employee attitudes. The findings of the MGA (Table 6) indicate no significant differences in the p-value or path coefficients between the two groups, meaning that there are no substantial differences in the influence of remote work on either variable.

Figure 2 illustrates the structural model results regarding reward allocation and employee attitudes, highlighting the indirect effects of the control variables.

5. Discussion

The current study aims to investigate the influence of remote work (RW) on organizational behavior, specifically its impact on reward allocation (salary and promotion) and employees’ attitudes (engagement, satisfaction, commitment, performance), while addressing the challenges of aligning HR and digital transformation.

To address this, a quantitative method was employed, using a survey to collect data from a sample of remote workers on the European continent. PLS-SEM software was used to analyze the collected data and test the hypotheses. The empirical data supported the proposed hypotheses (H1, H2, H3, H4, H5, and H6), indicating that remote work has significant and positive effects on work-related variables (i.e., SA, WFP, COM, ENG, JS, and WP) within the context of organizational behavior.

Through this study, it became clear that remote work is enabled by digital technologies (Gandrita et al., 2022; Marino-Romero et al., 2024), which allow SMEs to reduce overhead costs, offer greater employee flexibility, and implement new reward systems.

In this context, it is possible to understand that there was an emphasis on productivity, efficiency, and fairness in salary and promotion decisions, reflecting how digital transformation supports outcome-based evaluation that moves beyond traditional physical presence (Cenamor et al., 2019).

The results indicate that reward allocation focuses on exploring salary adjustments and workforce promotions within the context of remote work. Consistent with prior findings (Downes et al., 2024), salary remains a persistent concern for remote employees. To increase sustainability in this format, participants considered the need to provide greater flexibility, an efficient work environment, recognition of workplace challenges, and performance improvement as factors for increased remuneration. Moreover, there is a shared belief that salary adjustments should be performance-driven rather than based on physical presence, and organizations should account for the cost savings experienced by remote workers when evaluating compensation.

Regarding promotion opportunities, this remains an essential concern for remote employees. Participants highlighted that work in a remote setting should not hinder career progression; instead, it should provide equal opportunities, continually enhancing their visibility within the organization and the recognition of their contributions, regardless of physical presence. The encountered data is supported by Brenninkmeijer and Hekkert-Koning (2015), who discusses a newer approach to work being more flexible, adapted to employees’ needs, and open to new experiences. In hindsight, there is a shared belief that promotion prospects should be determined by factors such as value creation and performance, rather than the location where they were encountered.

Concerning employee attitudes, the data showed that engagement, satisfaction, and performance were the most relevant factors. Engagement in the organizational spectrum is viewed as associated with employee persistence, cognition, and a positive state of fulfillment (Schaufeli et al., 2002b). However, Schaufeli et al. (2002a). Through this dimension, the higher the engagement, the greater the impact on the worker, which reflects the ability to retain and attract new customers.

Despite the physical distance, most of our participants felt more connected to the organization, which aligns with social exchange theory (Joshi, 2025) and supports the development of a better work environment centered on their needs (May et al., 2004). This work format enhances employees’ sense of responsibility for daily tasks and encourages them to exert greater effort to achieve their work goals.

Additionally, engagement alone may not fully address concerns about the lack of supervision and control (Perry et al., 2018; Kirchner et al., 2021), but it can mitigate some issues by increasing accountability and teamwork. Through our research, we found that remote work has a positive impact on job satisfaction thanks to the benefits it offers. According to Gellis (2001) and Pugh (2016), job satisfaction refers to how employees feel about their work. When correlating this definition with remote work, we observed that employees were more satisfied when working from home, having a positive impact on overall satisfaction. Remote work allows employees to maintain a healthier balance between their personal and professional lives, increasing job satisfaction, and it is considered beneficial for reducing the likelihood of quitting.

Contrary to Molino et al. (2020) and Gualano et al. (2023), we discussed stress-related issues. Our research indicates that remote work enhances employees’ ability to manage stress, leading to increased feelings of fulfillment. The participants recognized that their organizations created a work environment that enhanced job satisfaction and that their concerns were taken into account. Moreover, managers need to consider that employees may hesitate to share personal information, lack interaction with team members, and feel isolated at work (Downes et al., 2024).

Furthermore, our results indicate that remote work has a positive influence on worker performance. Employee performance in the workplace is determined by the organization’s overall efficiency and productivity (Soliman et al., 2021). Our research demonstrates that RW improves workers’ overall performance by developing a stronger alignment with digital tools, achieving better outcomes within the organization, and improving reward mechanisms. The purpose of these actions is to divert the focus from physical presence to value creation. Moreover, there is an indication that age significantly moderates the relationship between variables like remote work and employee attitudes, whereas gender does not. The difference between these two is attributed to generational variations in digital literacy and fluency, work–life expectations, and career stage. Participants between the ages of 26 and 35 years old often perceive remote work as an opportunity to develop autonomy, flexibility, and professional growth, whereas older employees may associate it with challenges related to social interaction and adapting to digital workflows.

Although Ishii et al. (2023) considered remote work a benefit, it enables employees to deliver high-quality work when the right tools and environment are provided, allowing them to manage their time more effectively and complete their tasks and projects more efficiently. Even though these requisites have been met, employees still expect equitable compensation in remote work settings (Falk & Fischbacher, 2006).

These findings support and align with social exchange theory, which holds that salaries and promotions act as key signals of fairness. Rewards in remote contexts are based on outcomes rather than visibility, and the perceptions of equity increase, leading to motivation and satisfaction. Regarding dynamic capabilities theory, the study shows how SMEs leverage digital transformations in the reconfiguration of HR and reward systems with the ability to build adaptive and fair management practices.

6. Conclusions

Through the findings, it becomes apparent that digital transformation enables remote work by providing technological conditions for flexible work arrangements, which, in turn, influence how rewards are allocated and how workers respond to those practices. Salary adjustments and promotion opportunities are increasingly measured by the outcomes produced by the workers rather than physical presence, demonstrating a shift towards human resources practices and performance. Mechanisms like commitment, engagement, satisfaction, and performance support the principles of social exchange and equity theories, where fairness and reciprocity become a strong foundation for organizational relationships. However, remote work is seen as a tool that can enhance worker motivation, autonomy, and better alignment with the organizational goals. Factors such as engagement, job satisfaction, and performance have positive relationships with remote work, indicating that the more flexible the environment, the higher the ability to boost employee morale, dedication, and productivity.

These effects align with social exchange theory, showing that fairness perceptions and reciprocal exchanges remain central even when organizational interactions are virtual. In addition, the inclusion of control variables such as gender, education level, work experience, and SMEs status highlights that the level of competencies acquired, knowledge, and demographic factors play a role in understanding and shaping the guidelines for remote work. Conversely, age did not have a significant effect. Thus, SMEs undergo digital transformation, rely on digital literacy, adaptability, and accumulate expertise to implement better processes to improve performance. Demographic differences highlight the individual differences between employees but underscore the importance of human capital in enabling SMEs to leverage digital transformation for remote work practices and organizational competitiveness.

7. Implications

7.1. Theoretical Implications

This study contributes to the theoretical advancement of organizational behavior by empirically validating how remote work becomes a key influence on employee outcomes through the lens of reward allocation. First, this research broadens the understanding of how digital transformation is reshaping human resources practices, showing that salaries and promotions act as mechanisms linking remote work to employee attitudes. Second, traditional theories are often linked to reward allocation and face-to-face interactions. The findings suggest that, in remote work contexts, the mechanisms should focus on performance-based, meritocratic approaches, signaling a conceptual realignment in career progression. Third, according to Social Exchange Theory and Equity Theory, the findings underscore that remote work environments promote stronger engagement, satisfaction, and performance when employees perceive fairness in reward mechanisms, such as salary adjustments.

Furthermore, this study extends the existing theory by highlighting the diminished role of traditional promotion pathways in remote work contexts, where visibility is reduced and output-based assessment becomes more relevant. This action suggests a theoretical shift in how organizations conceptualize digital work settings, emphasizing meritocratic, performance-oriented metrics over face-to-face paradigms.

This research contributes to the discourse on how organizations may need to reconsider promotion criteria in the context of remote work. Visibility and proximity to managers are no longer considered direct factors in determining job satisfaction. Also, this research supports the notion that organizations should shift towards a result-oriented promotion system that emphasizes innovation and adaptability, particularly for remote workers.

7.2. Practical Implications

The practical implications provide actionable insights for policymakers and managers. With that in mind, this study has four implications. First, organizations should consider adjusting their compensation models to reflect the increase in productivity, performance, and flexibility associated with remote work, rather than relying on visibility or physical presence.

Second, the limited influence of remote work on promotion policies must be restructured to focus on measurable outcomes, creativity, and adaptability. Managers should develop new pathways that do not disadvantage remote employees due to their limited exposure. Third, strategies should address the specific needs of remote workers, such as providing the right tools and maintaining clear communication channels. This process includes adopting digital performance tracking tools, structured feedback systems, and skill-based career ladders.

Lastly, practical outcomes highlight the need for SMEs to invest in both digital literacy and infrastructure, as well as performance tools and the development of feedback systems, allowing systems to evolve fairly in remote work environments, while maintaining motivation and accountability.

8. Limitations and Future Research

This study has some limitations. One potential limitation is the self-reported nature of the data, which may have introduced social bias, leading participants to exaggerate their job satisfaction or commitment levels. To minimize this effect, the employment of a social desirability control scale (e.g., Marlowe—Crowne Social Desirability Scale) (Lambert et al., 2016) would help to reduce that bias. In this regard, we attempted to ask some questions while developing the questionnaire to avoid incorrect answers and eliminate bias. Therefore, in recognition of this limitation, and as a future step, we propose an experimental study involving multiple respondents to facilitate one-on-one conversations and further refine our findings. Finally, we realize that adding control variables considerably increases the size effect (f²).

In the realm of future research, remote work continues to hold great potential not only to transform the way organizations conduct business but also to be a factor in ensuring competitive advantage. Therefore, it is essential to continue exploring remote work. To be more supportive of employees, increase efficiency, and improve work–life balance.